add: launch appliation

QKTCC_simPennylane-26032022174332.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35f5b1f9bdf4513efe780a87620c18df40824c8c33d1c3c0502be9cf075a63e3
+size 44791559

README.md

@@ -1,10 +1,10 @@
 ---
-title: Quantum TL
-emoji: 📈
-colorFrom: green
-colorTo: purple
+title: QTL
+emoji: ⚡
+colorFrom: gray
+colorTo: red
 sdk: gradio
-sdk_version: 2.9.0
+sdk_version: 2.8.14
 app_file: app.py
 pinned: false
 license: mit

app.py

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+import torch
+import torchvision
+import gradio as gr
+import torch.nn as nn
+import pennylane as qml
+import matplotlib.pyplot as plt
+
+from pennylane import numpy as np
+from torchvision import transforms
+
+qubits = 4
+batch_size = 8
+depth = 6
+delta = 0.01
+
+is_cuda_available = torch.cuda.is_available()
+device = torch.device("cuda:0" if is_cuda_available else "cpu")
+
+if is_cuda_available:
+    print ("CUDA is available, selected:", device)
+else:
+    print ("CUDA not available, selected:", device)
+
+dev = qml.device("default.qubit", wires=qubits)
+
+def H_layer(nqubits):
+    for idx in range(nqubits):
+        qml.Hadamard(wires=idx)
+
+def RY_layer(w):
+    for idx, element in enumerate(w):
+        qml.RY(element, wires=idx)
+
+def entangling_layer(nqubits):
+    for i in range(0, nqubits - 1, 2):
+        qml.CNOT(wires=[i, i + 1])
+    for i in range(1, nqubits - 1, 2):
+        qml.CNOT(wires=[i, i + 1])
+
+@qml.qnode(dev, interface="torch")
+def quantum_net(q_input_features, q_weights_flat):
+    q_weights = q_weights_flat.reshape(depth, qubits)
+    H_layer(qubits)
+    RY_layer(q_input_features)
+
+    for k in range(depth):
+        entangling_layer(qubits)
+        RY_layer(q_weights[k])
+
+    exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(qubits)]
+    return tuple(exp_vals)
+
+class QuantumNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.pre_net = nn.Linear(512, qubits)
+        self.q_params = nn.Parameter(delta * torch.randn(depth * qubits))
+        self.post_net = nn.Linear(qubits, 2)
+
+    def forward(self, input_features):
+        pre_out = self.pre_net(input_features)
+        q_in = torch.tanh(pre_out) * np.pi / 2.0
+        q_out = torch.Tensor(0, qubits)
+        q_out = q_out.to(device)
+        for elem in q_in:
+            q_out_elem = quantum_net(elem, self.q_params).float().unsqueeze(0)
+            q_out = torch.cat((q_out, q_out_elem))
+        return self.post_net(q_out)
+
+def classify(image):
+    mhModel = torch.load("QKTCC_simPennylane-26032022174332.pth", map_location=device)
+    mMModel = torchvision.models.resnet18(pretrained=True)
+    for param in mMModel.parameters():
+        param.requires_grad = False
+    mMModel.fc = QuantumNet()
+    mMModel = mMModel.to(device)
+    qModel = mMModel
+    qModel.load_state_dict(mhModel)
+
+    from PIL import Image
+
+    data_transforms = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ])
+
+    PIL_img = image
+    img = data_transforms(PIL_img)
+    img_input = img.unsqueeze(0)
+
+    qModel.eval()
+    with torch.no_grad():
+        outputs = qModel(img_input)
+        base_labels = (("mask", outputs[0, 0]), ("no_mask", outputs[0, 1]))
+        expvals, preds = torch.max(outputs, 1)
+        expvals_min, preds_min = torch.min(outputs, 1)
+        if expvals == base_labels[0][1]:
+            labels = base_labels[0][0]
+        else:
+            labels = base_labels[1][0]
+        outp = "Classified with output: " + labels + ", Tensor: " + str(expvals) + " (" + str(expvals_min) + ")"
+        return outp
+
+out =  gr.outputs.Label(label='Result: ',type='auto')
+iface = gr.Interface(classify, gr.inputs.Image(type="pil"), outputs=out,
+            title="Quantum Layered TL RN-18 Face Mask Detector",
+            description="🤗 This proof-of-concept quantum machine learning model takes a face image input and detects a face that has a mask or no mask: ")
+
+iface.launch(debug=True)

requirements.txt

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+torch
+Pillow
+gradio
+pennylane
+matplotlib
+torchvision
+huggingface_hub